In industries that implement high-value machinery such as heavy industrial vehicles, managing the total cost of maintenance is of high importance because the replacement cost of such components is significant, as is the equipment downtime cost. Management of such machinery includes monitoring the health status of the vehicles and attempting to plan for upcoming component failures. Such attempts, however, face numerous challenges. For example, component failures appear to occur at random and are difficult to predict. Also, operating time alone does not characterize the life of a component accurately, as each vehicle may be utilized differently and/or utilized in different environments, resulting in different useful remaining lives.
Accordingly, a need exists for techniques to monitor and manage equipment maintenance across different failure causes including wear under normal operation, event-driven failures and failures due to manufacturing defects.